Research on social recommendation model based on enhanced neighbor perception

Recent years, people have been gradually influenced by online socialization. The emergence and development of Graph Neural Networks (GNN) has shown great advantages in mining implicit data and expressing node relationships, etc. However, due to the arbitrary nature of establishing neighborly relationships, the judging reliability of trusting relationships between neighbors is a difficult issue. Therefore, this paper proposes an innovative approach to obtain the valid neighbor relationships based on real user-item interactions and friend trust relationships in the dataset. The proposed method can address the impact of invalid neighbors on the social model and achieve the effect of enhancing neighbor perception. For neighbor interaction in the graph neural network model, this paper establishes the direct connection between users and items through mapping of multilayer perceptron firstly. Then it integrates neighbor similarity and neighbor sampling to mitigate the interference information of invalid neighbors and achieves the effect of enhancing the perceived information of neighbor interaction. Finally, this paper establishes the item social space and user social space with enhanced neighbor perception according to the dyadic nature and organically integrated them to enrich the social data. Comparison experiments are carried out based on two publicly available datasets Epinions and Ciao, the recommended method performs better than other social recommendation models with the MAE and RMSE values being improved by 0.81%-1.09% and 1.15%-1.41% respectively.